Our proposed architecture is depicted in Figure 1. It consists of a head, followed by 3 residual blocks (with skip connections), two degridding layers[1]} and a final upsampling layer. The residual blocks are similar to those in ResNet-18 and ResNet-34, but has fewer layers and makes use of dilated convolutions. The upsampling layer makes use of a sub-pixel convolution. The feature map with highest activation, formed after passing through each layer group is visualized in Figure 2.
